Results Summary: random ILR Augmentation Methods - Pseudo-count 1/max lib size
1 Logistic regression with \(\text{L}_1\) penalty
1.1 Interactive Summary Table
The table below shows the mean of the selected performance metric for the chosen augmentation factor.
1.2 Boxplots of performance metrics
One tabset with a box plot corresponds to a single augmentation factor in the section below.
1.2.1 ROC AUC
1.2.1.1 Density default
1.2.1.2 Density = 0.1
1.2.1.3 Density = 0.5
1.2.2 Misclassification Rate
1.2.2.1 Density default
1.2.2.2 Density = 0.1
1.2.2.3 Density = 0.5
1.3 Impact of the augmentation factor on the predictive perfromance
The plots below show the impact of the augmentation factor on the selected performance metric. Each tabset corresponds to a given density of the skew-symmetric matrix used for augmentation.
1.3.1 ROC AUC
1.3.2 Misclassification Rate
2 Random Forest
2.1 Interactive Summary Table
The table below shows the mean of the selected performance metric for the chosen augmentation factor.
2.2 Boxplots of performance metrics
One tabset with a box plot corresponds to a single augmentation factor in the section below.
2.2.1 ROC AUC
2.2.1.1 Density default
2.2.1.2 Density = 0.1
2.2.1.3 Density = 0.5
2.2.2 Misclassification Rate
2.2.2.1 Density default
2.2.2.2 Density = 0.1
2.2.2.3 Density = 0.5
2.3 Impact of the augmentation factor on the predictive perfromance
The plots below show the impact of the augmentation factor on the selected performance metric. Each tabset corresponds to a given density of the skew-symmetric matrix used for augmentation.
2.3.1 ROC AUC
2.3.2 Misclassification Rate
3 XGBoost
3.1 Interactive Summary Table
The table below shows the mean of the selected performance metric for the chosen augmentation factor.
3.2 Boxplots of performance metrics
One tabset with a box plot corresponds to a single augmentation factor in the section below.
3.2.1 ROC AUC
3.2.1.1 Density default
3.2.1.2 Density = 0.1
3.2.1.3 Density = 0.5
3.2.2 Misclassification Rate
3.2.2.1 Density default
3.2.2.2 Density = 0.1
3.2.2.3 Density = 0.5
3.3 Impact of the augmentation factor on the predictive perfromance
The plots below show the impact of the augmentation factor on the selected performance metric. Each tabset corresponds to a given density of the skew-symmetric matrix used for augmentation.
3.3.1 ROC AUC
3.3.2 Misclassification Rate
